Tribological Behavior Prediction for an Epoxy Aramid System Based on Mechanical and Thermal Properties Analyses
Taking into account that the tribological processes are a combination of many other processes the aim of this paper is building a neural network model based on mechanical and thermal properties for prediction of the tribological behaviour of an Epoxy- Aramidic composite system. The created epoxy based composites with aramidic powders, were tribological tested with diverse parameters in order to obtain follow properties: wear rate and friction coefficient. Bending and compression tests were performed for obtain main mechanical properties. Thermal tests were performed in order to obtain follow properties: specific heat, thermal conductivity and thermal expansions. With all the studied properties was created an Artificial Neural Network (ANN) model. The created ANN model can perform prediction for tribological behaviour of studied composites.
Keyword(s): tribological properties, mechanical properties, thermal properties, artificial neural network
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